Project Summary Project 1: Dynamic Host Responses During Resolution of HAP Hospital-acquired pneumonia (HAP) is one of the leading causes of death from nosocomial infections, with high rates of associated mortality. HAP due to Pseudomonas aeruginosa and Acinetobacter spp. is substantially more difficult to treat than other pathogens, with clinical failure rates as high as 50%. These rates persist despite optimization of antibiotic regimens, suggesting factors beyond antibiotic resistance contribute. In this Project, we will examine the host response to pneumonia. We hypothesize that persistent inflammation in the alveolus after appropriate antibiotic treatment contributes to clinical failure in patients with P. aeruginosa or Acinetobacter spp. pneumonia. We will test this hypothesis in three interrelated Specific Aims. Aim 1. To determine whether pathogen-associated changes in the transcriptomic signatures of alveolar macrophages and lymphocyte subsets over time predict outcome of severe pneumonia. Aim 2. To prospectively validate predictive host responses identified using an ecosystem-based modeling approach in a separate cohort of patients with severe pneumonia. Aim 3. To determine whether validated host responses predictive of clinical outcome are related to pneumonia endpoints in murine models. We will combine clinical data from the Electronic Health Record and integrate these clinical data with transcriptomic and epigenomic data obtained from flow sorted alveolar macrophages and Treg cells isolated from serial samples of NBBAL over the course of the patient's illness. Through the development of these predictive tools, the SCRIPT Systems Biology Center offers the potential to discover novel pathways that drive pneumonia pathobiology for therapeutic targeting, and the ability to revise understanding of pneumonia as a complex interaction between the host, pathogen, and microbiome.